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Charge Subtraction, Weizmann Clusterizer , and Pattern Recognition

Charge Subtraction, Weizmann Clusterizer , and Pattern Recognition. Mihael Makek Weizmann Institute of Science. HBD Fest, Stony Brook, 2010. Charge Subtraction. HBD occupancy in the most central events > 95 % Subtracted is the average charge per cell: on event-by-event basis

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Charge Subtraction, Weizmann Clusterizer , and Pattern Recognition

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  1. Charge Subtraction, Weizmann Clusterizer, and Pattern Recognition Mihael Makek Weizmann Institute of Science HBD Fest, Stony Brook, 2010

  2. Charge Subtraction • HBD occupancy in the most central events > 95 % • Subtracted is the average charge per cell: • on event-by-event basis • for each particular HBD module • taking into account pad area • Occupancy reduced to ~30 % in the most central events before subtraction: after subtraction: M. Makek - WIS

  3. HBD – charge subtraction • The subtraction is justified by the fact that the number of the scintillation photons grows linearly with number of tracks in CA • The example shows this for a single HBD module: • The bars represent the sigma of the gaussian fit to the average charge distribution (for given centrality) M. Makek - WIS

  4. Weizmann Clusterizer • Seed preblobs on pads with q > 3 pe • Add the six neighbors if they are above threshold (centrality dependant ~ 0-2 pe) • Merge preblobs if they have overlapping pads with the „main“ preblob • Match the cluster to the closest hit M. Makek - WIS

  5. Weizmann Clusterizer • The charge measured in HBD is obtained after matching cuts on hbddphi and hbddz • The background is estimated by swapping (CA tracks projected to a different module, x is swapped) • The background is normalized to the matching distributions tail-to-tail and subtracted signal+background background M. Makek - WIS

  6. Pattern Recognition • The random matching in HBD is due to the fact that CA electron tracks originating from conversions in and after the HBD backplane have no real matching hit in the HBD • The rejection of the random is achieved in three steps: • Requirying positional matching of the CA track projection and HBD cluster (3 sigma) • Rejection of 1 pad clusters • Rejection of clusters with the maximum pad charge below a certain threshold. This threshold is centrality dependent • The preblobs with qmax > 80 pe are rejected • Pattern recognition is done on preblob level peripheral central signal background M. Makek - WIS

  7. Pattern Recognition Before p.r. After p.r. • The pattern recognition significantly improves HBD signal to randoms, but costs the efficiency • The efficiency is estimated from MC simulation of single electrons embedded with HBD MB data S/R ~ 2.2 S/R ~ 1.1 M. Makek - WIS

  8. Pattern Recognition • variables monitored, but not as useful: qmax-qmin qmin/qmax qmin/qtot qmax/qtot • variables defined but not yet used: (yloc,zloc) of all pads in the cluster M. Makek - WIS

  9. Summary I. WIS Clusterizer with clustersize>1 && qmax<80 II. Pattern recognition + I. M. Makek - WIS

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